Nonlinear system identification and predictive control of a heat exchanger based on local linear fuzzy models

Abstract

This paper deals with identification and control of a highly nonlinear real world application, The performance and applicability of the proposed methods are demonstrated for an industrial heat exchanger. The main difficulties for identification and control of this plant arise from the strongly nonlinear center and the widely varying dead times introduced by different water flows. The identification of this three input one output process is based on the local linear model trees (LOLIMOT) algorithm. It combines efficient local linear least-squares techniques for parameter estimation of the local linear models with a tree construction algorithm that determines the structure of their validity functions. Furthermore, a subset selection technique based on the orthogonal least-squares (OLS) algorithm is applied for an automatic determination of the model orders and dead times. This strategy allows to design a wide range high accuracy nonlinear dynamic model of the heat exchanger on which the predictive control approach is based on. The nonlinear predictive control takes the speed and limit constraints of the actuator into account and leads to a high performance control over all ranges of operation.